173 lines
6.0 KiB
Plaintext
173 lines
6.0 KiB
Plaintext
TITLE by YOUR_NAME_HERE
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========================================================
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> **Tip**: You will see quoted sections like this throughout the template to
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help you construct your report. Make sure that you remove these notes before
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you finish and submit your project!
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> **Tip**: One of the requirements of this project is that your code follows
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good formatting techniques, including limiting your lines to 80 characters or
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less. If you're using RStudio, go into Preferences \> Code \> Display to set up
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a margin line to help you keep track of this guideline!
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```{r echo=FALSE, message=FALSE, warning=FALSE, packages}
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# Load all of the packages that you end up using in your analysis in this code
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# chunk.
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# Notice that the parameter "echo" was set to FALSE for this code chunk. This
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# prevents the code from displaying in the knitted HTML output. You should set
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# echo=FALSE for all code chunks in your file, unless it makes sense for your
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# report to show the code that generated a particular plot.
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# The other parameters for "message" and "warning" should also be set to FALSE
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# for other code chunks once you have verified that each plot comes out as you
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# want it to. This will clean up the flow of your report.
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library(ggplot2)
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```
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```{r echo=FALSE, Load_the_Data}
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# Load the Data
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```
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> **Tip**: Before you create any plots, it is a good idea to provide a short
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introduction into the dataset that you are planning to explore. Replace this
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quoted text with that general information!
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# Univariate Plots Section
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> **Tip**: In this section, you should perform some preliminary exploration of
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your dataset. Run some summaries of the data and create univariate plots to
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understand the structure of the individual variables in your dataset. Don't
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forget to add a comment after each plot or closely-related group of plots!
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There should be multiple code chunks and text sections; the first one below is
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just to help you get started.
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```{r echo=FALSE, Univariate_Plots}
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```
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> **Tip**: Make sure that you leave a blank line between the start / end of
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each code block and the end / start of your Markdown text so that it is
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formatted nicely in the knitted text. Note as well that text on consecutive
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lines is treated as a single space. Make sure you have a blank line between
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your paragraphs so that they too are formatted for easy readability.
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# Univariate Analysis
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> **Tip**: Now that you've completed your univariate explorations, it's time to
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reflect on and summarize what you've found. Use the questions below to help you
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gather your observations and add your own if you have other thoughts!
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### What is the structure of your dataset?
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### What is/are the main feature(s) of interest in your dataset?
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### What other features in the dataset do you think will help support your \
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investigation into your feature(s) of interest?
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### Did you create any new variables from existing variables in the dataset?
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### Of the features you investigated, were there any unusual distributions? \
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Did you perform any operations on the data to tidy, adjust, or change the form \
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of the data? If so, why did you do this?
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# Bivariate Plots Section
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> **Tip**: Based on what you saw in the univariate plots, what relationships
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between variables might be interesting to look at in this section? Don't limit
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yourself to relationships between a main output feature and one of the
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supporting variables. Try to look at relationships between supporting variables
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as well.
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```{r echo=FALSE, Bivariate_Plots}
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```
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# Bivariate Analysis
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> **Tip**: As before, summarize what you found in your bivariate explorations
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here. Use the questions below to guide your discussion.
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### Talk about some of the relationships you observed in this part of the \
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investigation. How did the feature(s) of interest vary with other features in \
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the dataset?
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### Did you observe any interesting relationships between the other features \
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(not the main feature(s) of interest)?
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### What was the strongest relationship you found?
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# Multivariate Plots Section
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> **Tip**: Now it's time to put everything together. Based on what you found in
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the bivariate plots section, create a few multivariate plots to investigate
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more complex interactions between variables. Make sure that the plots that you
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create here are justified by the plots you explored in the previous section. If
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you plan on creating any mathematical models, this is the section where you
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will do that.
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```{r echo=FALSE, Multivariate_Plots}
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```
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# Multivariate Analysis
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### Talk about some of the relationships you observed in this part of the \
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investigation. Were there features that strengthened each other in terms of \
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looking at your feature(s) of interest?
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### Were there any interesting or surprising interactions between features?
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### OPTIONAL: Did you create any models with your dataset? Discuss the \
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strengths and limitations of your model.
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------
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# Final Plots and Summary
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> **Tip**: You've done a lot of exploration and have built up an understanding
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of the structure of and relationships between the variables in your dataset.
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Here, you will select three plots from all of your previous exploration to
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present here as a summary of some of your most interesting findings. Make sure
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that you have refined your selected plots for good titling, axis labels (with
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units), and good aesthetic choices (e.g. color, transparency). After each plot,
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make sure you justify why you chose each plot by describing what it shows.
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### Plot One
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```{r echo=FALSE, Plot_One}
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```
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### Description One
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### Plot Two
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```{r echo=FALSE, Plot_Two}
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```
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### Description Two
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### Plot Three
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```{r echo=FALSE, Plot_Three}
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```
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### Description Three
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------
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# Reflection
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> **Tip**: Here's the final step! Reflect on the exploration you performed and
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the insights you found. What were some of the struggles that you went through?
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What went well? What was surprising? Make sure you include an insight into
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future work that could be done with the dataset.
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> **Tip**: Don't forget to remove this, and the other **Tip** sections before
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saving your final work and knitting the final report! |